Accounting for taste in housing decisions: Endogenous lifecycle stages
Bibliographic record
Abstract
In this paper, I study the demand for housing, the tenure choice, and the decision to “opt in " different lifecycle stages. Conventional studies often model a household’s housing demand and the tenure choice as a joint decision; lifecycle stages are seen as subsamples or categories, and these categories are “taste " variables that shift up or down the intercept of the housing demand function. Here, like the conventional studies, I treat housing demand and the tenure choice as a joint decision. Unlike the conventional studies, however, I argue that lifecycle stages are more than taste shifters. Instead, an individual chooses to opt in or out of some of these categories: to stay single or to form a couple household, or to raise children. These lifecycle decisions are jointly determined with the tenure choice and demand for housing. I use census micro data files for Ontario and Quebec to form a comparative case study, and set up a system of nine simultaneous equations: six regimes of housing demand, an equation to explain the tenure choice, and another two equations to describe lifecycle decisions. The results support the argument that households self-select themselves into lifecycle stages as they choose their tenure and housing expenditures. The study implies that demographics are important in understanding housing decisions. To the extent that they are important, demographics are not just factors affecting housing decisions. They are part of the decision. 1 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".